The book Design and Analysis of Learning Classifier Systems: A Probabilistic Approach by Jan Drugowitsch presents a machine learning approach to Learning Classifier Systems. In the author’s own words:

This book provides a comprehensive introduction to the design and analysis of Learning Classifier Systems (LCS) from the perspective of machine learning. LCS are a family of methods for handling unsupervised learning, supervised learning and sequential decision tasks by decomposing larger problem spaces into easy-to-handle subproblems. Contrary to commonly approaching their design and analysis from the viewpoint of evolutionary computation, this book instead promotes a probabilistic model-based approach, based on their defining question “What is an LCS supposed to learn?”. Systematically following this approach, it is shown how generic machine learning methods can be applied to design LCS algorithms from the first principles of their underlying probabilistic model, which is in this book  for illustrative purposes  closely related to the currently prominent XCS classifier system. The approach is holistic in the sense that the uniform goal-driven design metaphor essentially covers all aspects of LCS and puts them on a solid foundation, in addition to enabling the transfer of the theoretical foundation of the various applied machine learning methods onto LCS. Thus, it does not only advance the analysis of existing LCS but also puts forward the design of new LCS within that same framework.

Most people who know me will be aware that I am changing job shortly. I will be leaving the University of Bath from 30th September 2007, and will no longer be an academic. I am moving to Street in Somerset to become the Pastor of Street Baptist Church. I am still happy to answer any questions relating to my previous research, so do feel free to contact me via my new email address, which is linked from this site.

Alwyn, it has been a pleasure to be able to interact with you. I would like to wish you the best in your new endeavor, knowing that you will give your 100% as usual.

http://dita.ncsa.uiuc.edu/e2k/

Post-workshop proceedings will be published in Springer’s Lecture Notes in Computer Science / Artificial Intelligence series (LNCS/LNAI).

The call For Papers is available here.

Submission deadline is March, 16th.

The Tenth International Workshop on Learning Classifier Systems (IWLCS 2007) will be held in London, UK, July 7-8, 2007 during the Genetic and Evolutionary Computation Conference (GECCO-2007), July 7-11, 2007.

Since Learning Classifier Systems (LCSs) were introduced by John H. Holland as a way of applying evolutionary computation to machine learning problems, the LCS paradigm has broadened greatly into a framework encompassing many representations, rule discovery mechanisms, and credit assignment schemes. Current LCS applications range from data mining, to automated innovation, and to the on-line control of cognitive systems. LCS is a very active area of research that encompasses various system approaches. Wilson’s accuracy-based XCS system has received the highest attention and gained the highest reputation.

LCSs are benefiting from recent advances in machine learning, and reinforcement learning in particular, as well as in evolutionary computation. Novel insights in these two areas are continuously integrated into the LCS framework.

We invite submissions which discuss recent developments in all areas of research on, and applications of, Learning Classifier Systems. IWLCS is the event that brings together most of the core researchers in classifier systems. Moreover, a free introductory tutorial on LCSs is presented at GECCO 2007. The IWLCS workshop gives the opportunity also to researchers interested in LCS to get an impression of the current research directions in the field.

Submissions and Publication

There are two ways to submit papers (deadline March 16, 2007):

1. short papers (up to 4 pages in ACM format) or
2. full papers (up to 20 pages in Springer format)

All accepted papers may be presented orally at IWLCS. Accepted short papers will appear in the GECCO workshop volume. Proceedings of the workshop will be published on CD-ROM, and distributed at the conference. Authors of short papers will be invited after the workshop to submit revised (full) papers for publication in the post-workshop proceedings, in Springer LNCS/LNAI book series.

Accepted full papers will be published in the post-workshop proceedings. Authors of accepted full papers will be asked to provide a shorter 4-pages version for publication in the GECCO 2007 workshop proceedings.

The normal route is for authors to submit short papers and produce full papers after IWLCS for the post-workshop proceedings, incorporating feedback from reviewers and delegates. All submissions will be peer reviewed. Reviews of short papers will be mainly to provide feedback to enable the production of an improved full paper.

All papers should be submitted in PDF format and e-mailed to: esterb@salle.url.edu.

Important dates

• Paper submission deadline: Friday, March 16, 2007
• Notification to authors: Friday, March 30, 2007
• GECCO camera-ready material: by Wednesday, April 11, 2007
• Conference registration: Wednesday, April 11, 2007
• Workshop date: 7th or 8th July
• Extended paper submissions for LNCS/LNAI post-workshop proceedings: early fall 2007
• Notification of acceptance: late fall 2007
• LNCS/LNAI camera ready material: winter 2007/08

Committees

Organizing Commitee

• Jaume Bacardit, University of Nottingham (UK). E-mail: jqb@cs.nott.ac.uk
• Ester Bernadó-Mansilla, Universitat Ramon Llull (Spain). E-mail: esterb@salle.url.edu
• Martin V. Butz, Universitat Wurzburg (Germany). E-mail: mbutz@psychologie.uni-wuerzburg.de

Advisory Committee

• Tim Kovacs, University of Bristol (UK).
• Xavier Llorà, University of Illinois at Urbana-Champaign (USA).
• Pier Luca Lanzi, Politechnico de Milano (Italy).
• Wolfgang Stolzmann, Daimler Chrysler AG (Germany).
• Keiki Takadama, Tokyo Institute of Technology (Japan).
• Stewart Wilson, Prediction Dynamics (USA).

For more information please check here.

Since Learning Classifier Systems (LCSs) were introduced by Holland as a way of applying evolutionary computation to machine learning problems, the LCS paradigm has broadened greatly into a framework encompassing many representations, rule discovery mechanisms, and credit assignment schemes. Current LCS applications range from data mining to automated innovation to on-line control. Classifier systems are a very active area of research, with newer approaches, in particular Wilson’s accuracy-based XCS, receiving a great deal of attention. LCS are also benefiting from advances in the field of reinforcement learning, and there is a trend toward developing connections between the two areas. We invite submissions which discuss recent developments in all areas of research on, and applications of, Learning Classifier Systems. IWLCS is the only event to bring together most of the core researchers in classifier systems. A free introductory tutorial on LCS will be presented at GECCO 2007.

The final call for papers can be found here.

Part I. Knowledge representation

• 1. Analyzing Parameter Sensitivity and Classifier Representations for Real-valued XCS
  by Atsushi Wada, Keiki Takadama, Katsunori Shimohara, and Osamu Katai
  4399 - 001
• 2. Use of Learning Classifier System for Inferring Natural Language Grammar
  by Olgierd Unold and Grzegorz Dabrowski
  4399 - 018
• 3. Backpropagation in Accuracy-based Neural Learning Classifier Systems
  by Toby O’Hara and Larry Bull
  4399 - 026
• 4. Binary Rule Encoding Schemes: A Study Using The Compact Classifier System
  by Xavier Llorà, Kumara Sastry , and David E. Goldberg
  4399 - 041

Part II. Mechanisms

• 5. Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach Learning Classifier System
  by Jaume Bacardit and Josep Maria Garrell
  4399 - 061
• 6. Post-processing Clustering to Decrease Variability in XCS Induced Rulesets
  by Flavio Baronti, Alessandro Passaro, and Antonina Starita
  4399 - 081
• 7. LCSE: Learning Classifier System Ensemble for Incremental Medical Instances
  by Yang Gao, Joshua Zhexue Huang, Hongqiang Rong, and Da-qian Gu
  4399 - 094
• 8. Effect of Pure Error-Based Fitness in XCS
  by Martin V. Butz , David E. Goldberg, and Pier Luca Lanzi
  4399 - 105
• 9. A Fuzzy System to Control Exploration Rate in XCS
  by Ali Hamzeh and Adel Rahmani
  4399 - 116
• 10. Counter Example for Q-bucket-brigade under Prediction Problema
  by Atsushi Wada, Keiki Takadama, and Katsunori Shimohara
  4399 - 130
• 11. An Experimental Comparison between ATNoSFERES and ACS
  by Samuel Landau, Olivier Sigaud, Sébastien Picault, and Pierre Gérard
  4399 - 146
• 12. The Class Imbalance Problem in UCS Classifier System: A Preliminary Study
  by Albert Orriols-Puig and Ester Bernadó-Mansilla
  4399 - 164
• 13. Three Methods for Covering Missing Input Data in XCS
  by John H. Holmes, Jennifer A. Sager, and Warren B. Bilker
  4399 - 184

Part III. New Directions

• 14. A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients
  by Javier G. Marín-Blázquez and Sonia Schulenburg
  4399 - 197
• 15. Adaptive value function approximations in classifier systems
  by Lashon B. Booker
  4399 - 224
• 16. Three Architectures for Continuous Action
  by Stewart W. Wilson
  4399 - 244
• 17. A Formal Relationship Between Ant Colony Optimizers and Classifier Systems
  by Lawrence Davis
  4399 - 263
• 18. Detection of Sentinel Predictor-Class Associations with XCS: A Sensitivity Analysis
  by John H. Holmes
  4399 - 276

Part IV. Application-oriented research and tools

• 19. Data Mining in Learning Classifier Systems: Comparing XCS with GAssist
  by Jaume Bacardit and Martin V. Butz
  4399 - 290
• 20. Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule
  by Jaume Bacardit, David E. Goldberg, and Martin V. Butz
  4399 - 299
• 21. Using XCS to Describe Continuous-Valued Problem Spaces
  by David Wyatt, Larry Bull, and Ian Parmee
  4399 - 318
• 22. The EpiXCS Workbench: A Tool for Experimentation and Visualization
  by John H. Holmes and Jennifer A. Sager
  4399 - 343